Neuronal activation sequences in lateral prefrontal cortex encode visuospatial working memory during virtual navigation
Bibliographic record
Abstract
Abstract The brain can maintain and flexibly manipulate complex visuospatial information ‘in mind’, an ability known as working memory. The neural codes underlying this function have been a matter of debate. We simultaneously recorded the activity of hundreds of neurons in the lateral prefrontal cortex of monkeys during a visuospatial working memory task that required navigation in a virtual 3D environment. During task trials, animals perceived the location of a transient visual cue, remembered that location for a few seconds, and finally navigated toward it using a joystick to collect a reward. We found time boundary neurons that transiently activated just before the beginning and end of the memory period. Moreover, distinct neuronal activation sequences encoded specific remembered target locations in the virtual 3D environment, as viewed from the subject’s own visual perspective. This sequence code outperformed persistent firing codes for the remembered location during the virtual reality task, but did not occur during a classical working memory task using stationary stimuli and homogeneous displays. Finally, blocking NMDA receptors using low doses of ketamine selectively deteriorated the sequence code during the working memory task. Our results reveal a novel mechanism, NMDA-dependent neural activation sequences, for encoding visuospatial working memory in complex naturalistic environments. They further reveal the versatility and adaptability of neural codes supporting working memory function in the primate lateral prefrontal cortex.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".